The degree distribution of the generalized duplication model
Theoretical Computer Science
A clustering coefficient for weighted networks, with application to gene expression data
AI Communications - Network Analysis in Natural Sciences and Engineering
Characterization of Graphs Using Degree Cores
Algorithms and Models for the Web-Graph
CONTEST: A Controllable Test Matrix Toolbox for MATLAB
ACM Transactions on Mathematical Software (TOMS)
Detection of Locally Over-Represented GO Terms in Protein-Protein Interaction Networks
RECOMB 2'09 Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology
RECOMB'05 Proceedings of the 2005 joint annual satellite conference on Systems biology and regulatory genomics
Improved duplication models for proteome network evolution
RECOMB'05 Proceedings of the 2005 joint annual satellite conference on Systems biology and regulatory genomics
Comparative analysis of gene-coexpression networks across species
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
Quantifying systemic evolutionary changes by color coding confidence-scored PPI networks
WABI'09 Proceedings of the 9th international conference on Algorithms in bioinformatics
Protein-to-protein interactions: Technologies, databases, and algorithms
ACM Computing Surveys (CSUR)
A benchmark diagnostic model generation system
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on model-based diagnostics
Counting stars and other small subgraphs in sublinear time
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Recovering the toolchain provenance of binary code
Proceedings of the 2011 International Symposium on Software Testing and Analysis
RAGE - A rapid graphlet enumerator for large networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Assessing significance of connectivity and conservation in protein interaction networks
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
An important connection between network motifs and parsimony models
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Survey: Computational challenges in systems biology
Computer Science Review
Tutorial on biological networks
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
A novel approach to modelling protein-protein interaction networks
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Global Network Alignment In The Context Of Aging
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Characterizing the Topology of Probabilistic Biological Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Motivation: Networks have been used to model many real-world phenomena to better understand the phenomena and to guide experiments in order to predict their behavior. Since incorrect models lead to incorrect predictions, it is vital to have as accurate a model as possible. As a result, new techniques and models for analyzing and modeling real-world networks have recently been introduced. Results: One example of large and complex networks involves protein--protein interaction (PPI) networks. We analyze PPI networks of yeast Saccharomyces cerevisiae and fruitfly Drosophila melanogaster using a newly introduced measure of local network structure as well as the standardly used measures of global network structure. We examine the fit of four different network models, including Erdös-Rényi, scale-free and geometric random network models, to these PPI networks with respect to the measures of local and global network structure. We demonstrate that the currently accepted scale-free model of PPI networks fails to fit the data in several respects and show that a random geometric model provides a much more accurate model of the PPI data. We hypothesize that only the noise in these networks is scale-free. Conclusions: We systematically evaluate how well-different network models fit the PPI networks. We show that the structure of PPI networks is better modeled by a geometric random graph than by a scale-free model. Supplementary information: Supplementary information is available at http://www.cs.utoronto.ca/~juris/data/data/ppiGRG04/